Method and device for detecting a fault on a mechanical system comprising at least one rotating member

Abstract

The present invention relates to a method and a device for detecting a fault on a mechanical system (10) comprising a rotating member (15) and a vibration sensor (20) emitting a time-domain vibration signal. Said method comprises processing (100) said time-domain vibration signals emitted over a predetermined duration in order to transform said time-domain vibration signals firstly into angular vibration signals, then into frequency vibration signals. Then, series of amplitudes are generated, each series being associated with a specific frequency channel and comprising the amplitudes of said frequency vibration signals for this frequency channel. The frequency channels expressing a growth trend consistent with the desired fault are then identified.These frequency channels showing a growth trend are then compared with frequencies characteristic of a known and sought defect to verify the possible presence of such a defect on the mechanical system (10). Abstract figure: figure 1.

Description

Title of the invention: Method and device for detecting a fault on a mechanical system comprising at least one rotating member

[0001] The present invention lies in the field of systems for monitoring the operation of mechanical systems, in particular mechanical systems comprising at least one rotating member.

[0002] The present invention relates to a method for detecting a fault on a mechanical system comprising at least one rotating member as well as a device for detecting a fault configured to implement such a method.

[0003] Such a mechanical system comprises at least one rotating member, for example an input shaft and / or an output shaft. For the sake of simplification, a mechanical system comprising at least one rotating member is hereinafter referred to as a “mechanical system”.

[0004] For example, a mechanical system may comprise one or more bearings in order to guide the rotation of one or more rotating members. A bearing comprises, for example, a rolling bearing provided with one or more rows of rolling elements such as balls, rollers or the like.

[0005] Such a mechanical system may also be provided with at least one toothed wheel, a pinion, a toothed crown in order to provide a reduction or an increase in the rotation speed between two rotating members of the mechanical system, in particular between an input shaft and an output shaft.

[0006] Such a mechanical system may for example be provided with an epicyclic gear train making it possible to provide a large reduction ratio of the rotation speed between two rotating members of the mechanical system.

[0007] Such a mechanical system may for example be a gearbox or a power transmission box of a vehicle, in particular of an aircraft.

[0008] A breakdown or malfunction of such a mechanical system may occur, for example, following the appearance of a defect on a bearing, in particular a rolling bearing, and / or on a toothed wheel, a pinion, a crown gear. Such a defect may, for example, take the form of a crack, flaking, a fissure, or even a break, on a toothed wheel, a pinion, a crown gear or a bearing. Such a defect may also take the form of seizure of a rolling element.

[0009] Some monitoring systems designated by the acronym HUMS for “Health and Usage Monitoring System” in English aim to monitor one or more mechanical systems via different sensors by following the evolution of a set of monitoring indicators. These monitoring indicators Monitoring indicators are calculated from measurements from one or more sensors to characterize the state and operation of each mechanical system. For example, a monitoring indicator can be defined by a signal provided by one sensor or by combining signals from several sensors. Several monitoring indicators can also use measurements from a single sensor through various characteristics of the signal provided by this sensor, such as its time or frequency spectrum.

[0010] The evolution of each monitoring indicator can be compared to a detection threshold in order to detect or anticipate a possible fault or breakdown of the monitored mechanical system. The value of each detection threshold can be obtained by experience, by a statistical analysis of a history of measurements from several similar mechanical systems or by individual learning on a given mechanical system.

[0011] A monitoring indicator may take the form of a vibration indicator evaluated using a sensor comprising at least one accelerometer, a tachometer or a strain gauge for example. Such a monitoring indicator may in this case be equal to the maximum amplitude of a temporal vibration signal provided by an accelerometer for example.

[0012] For a vibration signal varying in the time domain, an indicator can be determined from statistical functions such as the quadratic mean, the crest factor, the asymmetry coefficient or the flattening coefficient of a distribution for example.

[0013] For a vibration signal expressed in the frequency domain, an indicator can be determined from the average frequency or the standard deviation frequency.

[0014] Other indicators can be constructed from decompositions of the signal in time and frequency such as the wavelet transform, the empirical mode decomposition or the short-term Fourier transform for example.

[0015] Most of these indicators are calculated from pre-processing carried out on a raw signal measured by a sensor in order to eliminate or significantly reduce noise and / or fluctuations in the speed of the monitored components. Among these pre-processings, such as angular resampling, calculations of the synchronous mean and the synchronous variance can also be used, with the aim of attenuating certain components of the raw signal such as for example a random component and / or noise.

[0016] The publications “A Review of Vibration Based Technique for Helicopter Transmission Diagnostics” by Samuel PD (Journal of sound and vibration, 2005), “A Review on Vibration-Based Condition Monitoring of Rotating Machinery” by Monica Tiboni (Department of Mechanical and Industrial Engineering, University of Brescia, 2022) and “Cepstrum Analysis and Gearbox Fault Diagnosis” by RB Randall (Bruel & Kjaer) describe methods for analyzing vibration signals measured on rotating machines equipped with gears to establish diagnoses of these machines and determine the possible presence of faults.

[0017] Furthermore, document CN 102426102 describes a method intended to detect the appearance of a crack on a transmission shaft carrying a gear by analyzing a vibration signal measured near the gear and a signal comprising information relating to the rotation speed of the transmission shaft.

[0018] The development of a sensitive and robust fault detection system is essential to prevent catastrophic failures of power transmission systems, particularly for aircraft. Any improvement in the detection of fault occurrence in a power transmission system significantly improves safety.

[0019] The prior art reports the monitoring of frequencies linked to a fault, frequencies known a priori, but does not take into account the linear combinations of these frequencies nor the overall evolution of their trends. In general, the trend of the energy of the frequency channels of the fault is followed but the evolution of the energy of the associated harmonics and the number of harmonics having significant energy is not taken into account.

[0020] The present invention therefore aims to propose an alternative method and an alternative device aimed at improving the detection of the appearance or presence of a fault on a mechanical system comprising at least one rotating member.

[0021] The present invention relates, for example, to a method for detecting a fault on a mechanical system, the mechanical system comprising at least one member rotating in rotation around an axis AX of rotation as well as a vibration sensor emitting a temporal vibration signal, an angular sensor emitting a temporal angular signal varying as a function of an angular position of the member rotating around the axis AX of rotation and a computer.

[0022] The vibration sensor(s) thus make it possible to measure characteristics, such as vibrations for example, of the mechanical system as a whole or of one or more of its components and to emit a temporal vibration signal %(?), namely in the time domain, carrying information relating to these characteristics, to the computer for example. The angular sensor makes it possible to measure an angular position of the member rotating around the axis AX of rotation, relative to a reference of the mechanical system, for example a frame or a casing of the mechanical system, and to emit a temporal angular signal t), namely in the domain temporal, carrying information relating to this angular position.

[0023] Such a mechanical system may comprise at least one of the following elements: at least one rotating member such as an input shaft and / or an output shaft, a rotational guide bearing and for example a bearing provided with rolling elements, a toothed wheel, a pinion, a toothed crown. Such a mechanical system may for example be a gearbox or a power transmission box of a vehicle.

[0024] The method according to the invention comprises the following steps: - processing of temporal vibration signals xm (?) emitted by the vibration sensor comprising the following sub-steps: • emission of M temporal vibration signals xm ( t ) by the vibration sensor, M being greater than or equal to 2, m being a positive integer varying from 1 to M, “t” being the operating time of the mechanical system, • first transformation, with the calculator, of the M temporal vibration signals x^t) into M angular vibration signals Xm{9) as a function of the temporal angular signal Bm[ t \ "9" being an angle of the organ rotating around the axis AX of rotation, • second transformation, with the calculator, of the M angular vibration signals xm(0) into M frequency vibration signals ( / ) By a frequency oscillator &, where “fg” is a frequency channel, • first determination of frequency channels of trend f as a function of the M frequency vibration signals (yj, a frequency channel of trend f being a frequency channel fg for which the M frequency vibration signals ( / ) meet a selection criterion relating to a growth trend, - analysis, with the calculator, of the trend frequency channels f comprising the following sub-steps: • comparison of each trend frequency channel f with linear combinations of fault frequencies fd relating to the fault sought, the linear combinations respectively comprising sums of several of the fault frequencies fd multiplied by integer coefficients ni, • second determination of fault frequency channels f as a function of the comparison, a fault frequency channel f being a trend frequency channel f whose differences with the linear combinations are lower than a comparison threshold, - calculation, with the calculator, of an optimal presence indicator I of the fault on the mechanical system as a function of the number of fault frequency channels f and the number of linear combinations of fault frequencies fd, - triggering of an alert of presence of a fault as a function of a comparison between the optimal presence indicator I of the fault and a threshold of presence of a fault.

[0025] A vibration sensor, within the scope of the invention, may comprise, for example, an accelerometer, a tachometer, an encoder-type sensor measuring an angular position of a shaft, a strain gauge, or the like. Such a vibration sensor makes it possible to measure temporal vibration signals, comprising, for example, acceleration signals or speed signals. Such a vibration sensor may be positioned at different positions in the mechanical system and with various orientations. Such a vibration sensor may preferably be positioned on or near a rotating member, for example an input or output shaft, or a specific element to be monitored, such as a bearing, a toothed wheel, a pinion, or a toothed crown, for example. The vibration sensor may thus emit a temporal angular signal carrying information relating to the vibrations captured over time.

[0026] “Position” means the location where the vibration sensor is arranged on the mechanical system. “Orientation” means the angles of one or more preferred measurement directions of the vibration sensor.

[0027] The angular sensor can be positioned close to the rotating member whose angular position it is to measure. The angular sensor can thus emit a temporal angular signal carrying information relating to the variation of this angular position over time.

[0028] The temporal vibration signal and the temporal angular signal may be signals formed by raw measurements emitted respectively by the vibration sensor and the angular sensor or by measurements obtained by more or less complex signal processing carried out by the computer or by a computer integrated into the corresponding sensor from such raw measurements, for example via standard filtering or sampling, or even the application of transformations.

[0029] The temporal vibration signal and the temporal angular signal can be generated simultaneously, or even synchronously.

[0030] Thus, the vibration sensor emits M time-dependent vibration signals xm(t ) during a predetermined duration of operation of the mechanical system. The time-dependent vibration signals / ) are generally emitted at substantially equal regular intervals and each include information relating to the vibrations experienced by a part or the whole of the mechanical system for a predefined measurement time Tf of the mechanical system. This measurement time Tf can be equal to several seconds, or even a few minutes and cover several operating cycles of the mechanical system, for example ten rotation cycles of the rotating member around the rotation axis AX, the angle 0 of the rotating member being in this case equal to 3600°. However, time-domain vibration signals xm(t) can also be emitted with different intervals between them. For example, a time-domain vibration signal xm(t) can be emitted systematically after the start-up of the mechanical system, possibly once a stabilized rotation speed has been reached.

[0031] At the same time, the angular sensor emits, for the predetermined duration, M temporal angular signals 0(t) which can be associated and synchronized with the temporal vibration signals xm(t) •

[0032] Each time vibration signal xm(t) and each time angular signal are emitted during a measurement duration Tf, for example equal to a few tens of seconds or a few minutes. The time-domain vibration signals xm(t) and the time-domain angular signals t) can be stored in a memory of the computer or in a memory connected to the computer.

[0033] Then, the computer applies two successive transformations to the M time-domain vibration signals x^t ) in order to transform them initially, in the usual way, into M angular vibration signals and then to transform, using a frequency operator $, the M angular vibration signals xm( 6} into M frequency vibration signals ( / , ) ' Each frequency vibration signal x ) therefore comes from a time-domain vibration signal x,„(z) and therefore from a vibration measurement carried out on the mechanical system. The M frequency vibration signals ( / ) are therefore spectral signals discretized according to the angular position 0 of the rotating member and vary according to a frequency channel f & which is linked to this angular position 0. The frequency channels fe are therefore relative to the angular positions of the rotating member and not relative to time, like a frequency conventionally used.The frequencies of these discretized spectral signals are referred to as “frequency channels f0”.

[0034] The angular vibration signals x / n(0) and the frequency vibration signals fj can be stored in the memory of the computer or in the memory connected to the computer.

[0035] One or more frequency channels of trend f indicating a trend of growth of the M frequency vibration signals are then determined according to a selection criterion. Thus, the M frequency vibration signals xn^fj associated with a trend frequency channel / indicate a growth trend and satisfy this selection criterion relating to this growth trend. The expression "the M frequency vibration signals (yj indicate a tendency of growth” means that, for the same frequency channel fe, the amplitudes of the M frequency vibration signals) are increasing from the first vibration signal frequency x^f) towards 'c last vibration signal frequency x^f according to a curve whose characteristic may be this growth trend.

[0036] Each frequency channel of tendency f is therefore a frequency channel f0 whose amplitudes xm[f of the frequency vibration signals yj relating to this frequency channel of tendency f increase during the operation of the mechanical system. Each frequency channel of tendency f is therefore a frequency channel fg which characterizes an increase in vibrations during the operation of the mechanical system. This increase in vibrations can thus potentially characterize the appearance, or even the presence, of a fault on the mechanical system. The frequency channels of tendency f can be stored in the memory of the computer or in the memory connected to the computer.

[0037] The computer then analyzes these trend frequency channels f by comparing each trend frequency channel f with linear combinations of fault frequencies fd relating to the specific fault sought, and determines fault frequency channels f following this comparison. The fault frequency channels f can be stored in the memory of the computer or in the memory connected to the computer.

[0038] The fault frequencies fd relating to the fault sought are known and defined previously by feedback from experience, by tests, or even by simulations. The fault frequencies fd may have been previously stored in the computer memory or in the memory connected to the computer. Indeed, a fault on a mechanical system often generates amplitude and frequency modulations on the vibrations of this mechanical system and gives rise to a set of excited frequency channels whose frequency can be expressed as a linear combination of the fault frequency(ies) f d.

[0039] Each fault frequency fda for example has been identified on a system mechanical system presenting the desired defect and for which the amplitudes of the measured vibration signals are significant. The defect frequency(ies) fd associated with a specific desired defect can be contained in a defect model. In this way, several defect models can exist, making it possible to characterize respectively different types of defects likely to appear on the mechanical system.

[0040] Furthermore, a fault model may be parametric. The fault frequencies fd associated with the fault considered may then be variable depending on operating parameters of the mechanical system, such as the operating phase of the mechanical system, and / or the torque of a rotating member, or even the temperature inside the mechanical system for example. The fault frequencies fd may also be variable depending on parameters external to the mechanical system, such as the temperature for example.

[0041] Each linear combination comprises a sum of several of these fault frequencies fd multiplied by integer coefficients n;. The use of these linear combinations therefore makes it possible to compare the trend frequency channels f with the known fault frequencies fd as well as with some of their harmonic frequencies, according to the coefficients n; used.

[0042] A fault frequency channel f is thus determined and equal to a trend frequency channel f whose differences with the linear combinations are less than a comparison threshold, or even zero. In this way, the trend frequency channels f are selected which are linear combinations or "almost" linear combinations of the fault frequencies fd of the fault model relating to the desired fault.

[0043] One or more fault frequency channels f can thus be determined to be equal respectively to one or more fault frequency channels f when these coincide, up to the comparison threshold, with a fault frequency fd, with one of its harmonic frequencies or with linear combinations of these fault frequencies fd. The comparison threshold may have been stored beforehand in the memory.

[0044] The determination of one or more fault frequency channels f thus characterizes a risk of the presence of a fault on the mechanical system.

[0045] To confirm this risk, the computer calculates an optimal presence indicator C H-characterizing this risk of presence of a fault on the mechanical system as a function of the number of fault frequency channels f and the number of linear combinations of fault frequencies fd; This optimal presence indicator can then be compared to a threshold of presence of a fault to confirm or deny this risk. The threshold of presence of a fault may have been previously stored in the memory.

[0046] Thus, a fault presence alert is triggered based on a comparison between the fault presence indicator Intimai and the fault presence threshold. This fault presence alert can be triggered by an alert generator. This fault presence alert can be visual, audible and / or haptic for example.

[0047] The method according to the invention thus makes it possible to reliably determine a detection of the appearance of a fault on the mechanical system or its presence as soon as frequency channels exhibit a significant growth trend, by identifying these frequency channels, then comparing them with linear combinations of known fault frequencies fd associated with the fault being sought. Consequently, the detection of this fault can be anticipated, making it possible to deal with this fault as early as possible, thus avoiding for an aircraft the risks of interruption or cancellation of an aircraft flight, emergency landing, or even an accident.

[0048] The method according to the invention may comprise one or more of the following characteristics, taken alone or in combination.

[0049] According to one example, the comparison threshold may be predetermined and may for example be equal to a frequency resolution associated with the discretized frequency vibration signals.

[0050] According to another example compatible with the previous ones, the predetermined duration during which the vibration sensor emits the M temporal vibration signals xm ( t ) can be sliding.

[0051] In this way, when the vibration sensor emits an (M+1)th temporal vibration signal Xw+1( / ), the method is then applied to M temporal vibration signals Xm(t), m being between 2 and M+1. The (M+1)th temporal vibration signal xM+\ ( 0 is P31 example stored in the memory and the first temporal vibration signal can optionally be deleted from the memory.

[0052] The predetermined duration may be, for example, equal to several hours, or even several days of operation depending on the mechanical system concerned. The interval between two temporal vibration signals may be, for example, equal to a few tens of minutes or several hours of operation of this mechanical system.

[0053] According to another example compatible with the previous ones, a number of the coefficients n; integers can be limited, and consequently the values ​​of the coefficients n; integers can be bounded.

[0054] Indeed, the higher the value of the coefficients n;, the greater the risk that frequency channels meeting the criterion of fault frequency channels f are confused with other physical phenomena of the system and, in fact, the risk of detecting false faults also increases.

[0055] A limited number of coefficients n; also makes it possible to limit the number of linear combinations and, consequently, the calculation time of the fault frequency channels f.

[0056] According to another example compatible with the previous ones, the presence indicator Ioptimal of the fault can increase when the number of fault frequency channels f increases and the alert of presence of a fault can be triggered if the presence indicator Ioptimal is greater than the threshold of presence of a fault.

[0057] For example, the presence indicator Ioptimal of the fault may be equal to a ratio of a number of fault frequency channels f by a total number of linear combinations of the fault frequencies fd and the alert of presence of a fault may be triggered if the presence indicator Ioptimal is greater than the threshold of presence of a fault.

[0058] The presence indicator Ioptimal is thus normalized and between 0 and 1.

[0059] According to another example compatible with the previous ones, the first determination of the frequency channels of tendency f can comprise the following sub-steps: - generation, with the calculator, of at least one series of amplitudes Xf associated with a frequency channel fg and comprising M amplitude values ​​belonging respectively to the M frequency vibration signals and relative to the frequency channel fg, the number of amplitude series Xf being equal to the number of frequency channels f - calculation, with the calculator, of a trend indicator r ( f \ for 1 trend \J g J each frequency channel fg as a function of the amplitude series associated with the frequency channel fg, and - first determination, with the calculator, of trend frequency channels f , each trend frequency channel f being a frequency channel fg meeting the selection criterion, this selection criterion then being relative to the trend indicator j ( f \- 1 trend \J p )

[0060] Each series of amplitudes X / thus comprises as many amplitude values ​​as temporal vibration signals have been emitted over the predetermined duration. Each series of amplitudes Xf thus characterizes the “energy levels” associated with the frequency channel fg with which this series of amplitudes Xf is associated. Two distinct series of amplitudes Xf are respectively associated with two distinct frequency channels fg.

[0061] The first determination then makes it possible to identify, among the frequency channels fg, the frequency channels of tendency f for which these energy levels are increasing significantly over the predetermined duration. Indeed, an in- trend indicator iff} reflecting the growth trend of channels fre-1 trend \J fi) trend variables f, meets and satisfies the selection criterion, and can reflect the appearance of the fault considered.

[0062] Indeed, for almost all faults, the energy levels of the frequency channels associated with a fault increase slowly for a long time at the appearance of the fault and then gradually increase more quickly until a rupture. The purpose of the trend indicator is therefore to identify the frequency channels which are in the slow growth phase. This makes it possible to identify them well before the rupture occurs.

[0063] Each trend frequency channel f is a frequency channel fg meeting the selection criterion, this selection criterion then being relative to the trend indicator i ( f V trend \J fi )

[0064] According to a first selection criterion, each trend frequency channel ft is a frequency channel f „ for which said trend indicator j ( f 1 is greater than 11 1 trendfi) at the trend threshold. Each trend frequency channel f is, according to this first selection criterion, a frequency channel fg whose amplitudes Xm f of the frequency vibration signals ( f ) relating to this trend frequency channel ft increase during the operation of the mechanical system more quickly than the amplitudes of a predetermined curve associated with the trend threshold.

[0065] The first determination then makes it possible to identify, among the frequency channels fg, the frequency channels of trend f for which these energy levels are increasing significantly during the predetermined duration, which can reflect the appearance of the fault considered if a growth trend of these energy levels is greater than the trend threshold.

[0066] According to a second selection criterion, the trend frequency channels f are the P frequency channels fg corresponding to the P largest trend indicators fj, P being an integer greater than one. P is for example equal to 5. In this case, the trend frequency channels f are for example the frequency channels fg whose M temporal vibration signals xm{ t) are characterized by the strongest growth.

[0067] Each frequency channel of tendency f is, according to this second selection criterion, a frequency channel fg whose amplitudes xm( f ) of the frequency vibration signals h( / ) related to this frequency channel of tendency ft increase most rapidly at during the operation of the mechanical system. The first determination then makes it possible to identify, among the frequency channels, the frequency channels with trend f, for which these energy levels increase most rapidly over the predetermined duration, and likely to reflect the appearance of the fault considered.

[0068] Whatever the selection criterion used, each frequency channel of trend f is therefore a frequency channel fe which characterizes an increase in vibrations during operation of the mechanical system. This increase in vibrations can thus potentially characterize the appearance, or even the presence, of a fault on the mechanical system. The frequency channels of trend f can be stored in the memory of the computer or in the memory connected to the computer.

[0069] The first and second selection criteria can be used independently or in a complementary manner, namely simultaneously.

[0070] Furthermore, each amplitude value of said at least one series of amplitudes Xf can be associated with an instant tm of a start of emission of the temporal vibratory signal Xm (0 from which the amplitude value originates.

[0071] Alternatively, the instant tm can also correspond to the instant of start of a measurement of the vibrations undergone by the mechanical system, the time-dependent vibration signal ) being the bearer of information relating to these vibrations. The start of emission of the time-dependent vibration signal xm(t) and the start of a measurement of the vibrations undergone by the mechanical system can be substantially the same and simultaneous.

[0072] Furthermore, the calculation of a trend indicator jlf ] may include the 1 (rendgy following sub-steps: - third transformation of said at least one series of amplitudes Xf by a transformation function $ into at least one trend series according to the relation - construction of at least one trend line from said at least one trend series by linear regression, according to the relationship

[0073] where “r” is a variable,

[0074] “hf (r)” is a trend line,

[0075] “Af” is a slope coefficient of the trend line,

[0076] “Bf” is an ordinate at the origin of the trend line, - estimation of a noise coefficient ar for each trend line / jy (r ), according to the relation M , ,2 WJ = ( A ^ x ) I

[0077] where “* is the sum function with m varying from 1 to M,

[0078] “11” is the absolute value function, - calculation of the trend indicator Itrend as a function of the noise coefficient relative to the trend line for each frequency channel fff, according to the relation jtr \ _ ,

[0079] where “a” and “[3” are predetermined parameters.

[0080] The trend indicator Itrend is thus calculated as a ratio between the slope of the linear regression and the noise level.

[0081] The parameters a and [3 make it possible to weight the sensitivity to noise, namely the influence of this noise.

[0082] The parameters a and [3 can be specific to each defect sought.

[0083] The transformation function can for example be the unit function or the logarithm function or another mathematical function. The aim of this third transformation is to obtain a globally linear evolution of the amplitude values ​​after application of the transformation function $ during the phase of slow growth of these amplitude values ​​in the event of the appearance of a fault. The choice of the function & makes it possible in particular to obtain good sensitivity either at the beginning or at the end of propagation of the fault. The construction of a trend line by linear regression is then more precise and more reliable to obtain an evolution slope and an estimation of the noise coefficient and then to construct a robust and simple Itrend trend indicator.

[0084] Alternatively, transformations leading to obtaining a characteristic evolution curve linked to the nature of evolution of the amplitude values ​​associated with the frequency channels of the desired fault can be used. This characteristic evolution curve can be approached using curve fitting methods by a reduced parametric expression, specific to this characteristic evolution curve. Thus, the parameters of the characteristic evolution curve and the estimation of the noise coefficient would also make it possible to construct a robust trend indicator Itrend to identify the frequency channels of trend f.

[0085] The method according to the invention may also comprise a step of filtering the vibration signals in order to filter the vibration signals, in particular when these vibration signals are noisy. This filtering step may be applied both to the time vibration signals xm(t), the angular vibration signals xm(0) or the frequency vibration signals yj, as well as to the amplitude series X / .. This filtering step makes it possible to smooth the vibration signals and / or the series in such a way that usual by applying for example median filtering or other existing filters.

[0086] The present invention also relates to a computer program comprising instructions which, when the program is executed, cause it to implement the method described above. The computer program may for example be executed by a computer. The instructions are for example stored in a memory of the computer or connected to the computer.

[0087] The present invention also relates to a monitoring device for monitoring a mechanical system and configured for implementing the method described above and thus for detecting the presence of a fault on a mechanical system comprising at least one member rotating in rotation around an axis AX of rotation. The monitoring device comprises at least one vibration sensor emitting a temporal vibration signal, an angular sensor emitting a temporal angular signal varying as a function of an angular position of the member rotating around the axis AX of rotation and a computer.

[0088] The present invention also relates to a mechanical system comprising at least one member rotating in rotation around an axis AX of rotation and a monitoring device as previously described for monitoring the mechanical system and for detecting the presence of a fault on the mechanical system.

[0089] This mechanical system can for example be a power transmission box of a vehicle, and of an aircraft in particular.

[0090] The present invention also relates to a power transmission box comprising such a mechanical system.

[0091] The present invention may finally relate to an aircraft comprising such a power transmission box.

[0092] The invention and its advantages will appear in more detail in the context of the description which follows with examples given for illustrative purposes with reference to the appended figures which represent: - [Fig.l], a schematic side view of an aircraft, and - [Fig.2], a diagram illustrating a method for detecting a fault in a mechanical system.

[0093] Elements present in several distinct figures are assigned a single reference.

[0094] [Fig.l] represents a vehicle 1, and a rotary wing aircraft of the rotorcraft type in particular. This vehicle 1 comprises a mechanical system 10 provided with one or more rotating members 15 rotating around an axis AX of rotation. A rotating member 15 may for example comprise an output shaft or an input shaft.

[0095] Such a mechanical system 10 may comprise one or more rotational guide bearings, for example for the rotational guidance of at least one rotating member. bearing comprises, for example, a rolling bearing fitted with rolling elements.

[0096] Such a mechanical system 10 may also comprise, for example, at least one toothed wheel, a pinion, a toothed crown, fixed or mobile.

[0097] This mechanical system 10 may be, for example, a gearbox or a power transmission box of the rotary-wing aircraft 1. This mechanical system 10 may be connected, for example, to one or more engines 2, via respectively one or more input shafts, and may drive a rotor in rotation, such as, for example, a main rotor 3 via an output shaft, or possibly even an auxiliary rotor 4 as shown in [Fig. 1].

[0098] Alternatively, such a mechanical system 10 may be arranged in a gearbox or a power transmission box of a vehicle 1 or any other mechanical equipment.

[0099] Whatever its arrangement, the mechanical system 10 also comprises one or more vibration sensors 20, an angular sensor 25 measuring an angular position of a rotating member 15 around an axis AX of rotation and a computer 5. The mechanical system 10 may comprise several angular sensors 25 arranged respectively on several distinct rotating members 15.

[0100] Each vibration sensor 20 can measure and / or emit a temporal vibration signal relating to a vibration behavior of the mechanical system 10 as a whole, or to a particular vibration behavior of a rotating member 15, of a bearing or even of a gear for example. The temporal vibration signal carries information relating to the vibrations of the mechanical system 10 or of one of its components. The vibration sensor(s) 20 can comprise for example an accelerometer, a tachometer, an encoder type sensor and / or a strain gauge.

[0101] The angular sensor 25 measures an angular position of a rotating member 15 around an axis AX of rotation. The angular position of the rotating member 15 around an axis AX of rotation can be defined relative to a reference frame of the mechanical system 10, for example a casing of the mechanical system 10. The time signal can carry information relating to the angular position of a rotating member 15 around an axis AX of rotation.

[0102] The angular sensor 25 may comprise an angular position sensor directly measuring a time signal indicating the variation of the angular position of the rotating member 15 as a function of time.

[0103] Alternatively, the angular sensor 25 may comprise an angular speed sensor or an angular acceleration sensor measuring a time signal relating respectively to an angular speed or to an angular acceleration which must undergo a simple integration or a double integration, in order to generate a time signal providing the variation of the angular position of the rotating member 15 as a function of the time. This simple or double integration can be performed by the computer 5. This simple or double integration can also be performed by a computer integrated into the angular sensor 25.

[0104] The computer 5 may comprise at least one processor and at least one memory, at least one integrated circuit, at least one programmable system or at least one logic circuit, these examples not limiting the scope given to the expression “computer”. The computer 5 may also be connected to a memory by a wired connection or a wireless connection.

[0105] The memory may for example store instructions or algorithms relating to the performance of a fault detection method on the mechanical system 10 and one or more thresholds corresponding to this method. The memory may also store a computer program intended to be executed by the computer 5 in order to implement the fault detection method.

[0106] The vibration sensor(s) 20 as well as the angular sensor 25 and the computer 5 may be part of a monitoring device 9 of the mechanical system 10 intended to monitor the mechanical system in order to detect and identify a risk of appearance or presence of a fault likely to cause a breakdown or malfunction of the mechanical system 10.

[0107] The computer 5 can be dedicated to the monitoring device 9 or be shared with other devices of the aircraft 1.

[0108] The fault detection method according to the invention comprises four main steps as shown in the diagram of FIG. 2. A first main processing step 100 processes the time-domain vibration signals xm ( t ) in order to determine frequency channels of tendency f likely to correspond to a fault present on the mechanical system 10. A second main analysis step 200 carries out an analysis of these frequency channels of tendency f to identify among them frequency channels of fault f , corresponding to the fault sought.

[0109] Then, a third main calculation step 300 allows the calculation of an optimal presence indicator I of the fault sought on the mechanical system 10 and a fourth main triggering step 400 triggers an alert of presence of a fault as a function of this optimal presence indicator I of the fault and of a threshold of presence of a fault.

[0110] In this way, the detection of a fault on the mechanical system 10 can be reliably anticipated from the first signs of its appearance, the presence of this fault being able in fact to be detected through the vibrations of this mechanical system 10.

[0111] The first main processing step 100 comprises several sub-steps.

[0112] Thus, during a transmission step 120, M temporal vibration signals are emitted by the vibration sensor 20, for example over a predetermined duration of operation of the mechanical system 10. This predetermined duration may be sliding. M is an integer greater than or equal to 2, and m is an integer varying from 1 to M identifying the number of the temporal vibration signal xm(t) and therefore the number of the measurement of the vibrations of the mechanical system 10. “t” is the duration of operation of the mechanical system 10. The duration t of operation is considered to be stopped when the mechanical system 10 is not in operation.

[0113] Preferably, the M time-dependent vibration signals xm(t) correspond to vibration measurements carried out at operating speeds of the mechanical system 10 that are substantially similar and close. Operating speeds are considered to be close, for example, if the rotation speed differences of the rotating member 15 are less than 10% and if the torque differences of the rotating member 15 are less than 30%.

[0114] Each time vibration signal xm(t) corresponds to a measurement of the vibrations of the mechanical system 10 carried out during a measurement duration Tf, preferably identical for all the time vibration signals xm(t).

[0115] Simultaneously, M temporal angular signals are emitted by the angular sensor 25, “9” being an angle of the rotating member 15 around the axis AX of rotation.

[0116] The M time-dependent vibration signals xm(t) and the M time-dependent angular signals 0m(t) are transmitted, in the form of electrical or optical, analog or digital signals, by a wired or wireless link, to the computer 5.

[0117] Alternatively, the vibration sensor(s) 20 and the angular sensor 25 can continuously transmit a time vibration signal x(t) and a time angular signal #( / ) to the computer 5 which records at regular intervals the M time vibration signals xm(t) and the M time angular signals 0(t) in the memory.

[0118] Then, during a first transformation step 140, the computer 5 transforms, in a known manner, the M temporal vibration signals xm(t) into M angular vibration signals xm(0) as a function of the temporal angular signal #(?).

[0119] This transformation of the time vibration signal xm (?) from the time domain to the angular domain advantageously makes it possible to associate the variations of the angular vibration signal with the angular positions of the rotating member 15, and therefore with positions of the elements of the mechanical system 10. This first transformation step 140 therefore allows a resampling of the time vibration signal xm(t) in the angular domain in order to attenuate in particular the effect of the regime fluctuations. of rotation of the rotating member 15 on the vibration measurements. Indeed, the rotation speed of the rotating member 15 is not systematically constant, in particular during transient phases, for example during the start-up of the mechanical system 10 or changes in speed and in the event of high stresses on the mechanical system 10.

[0120] Then, during a second transformation step 150, the computer 5 transforms the M angular vibration signals xm ( 0 ) into M frequency vibration signals j Pæ" a frequency operator 9. This second transformation can be formulated according to the relation xm ( 3) ) ' °ù * fg * cst a frequency channel. The frequency channels fe are derived from the second transformation using the frequency operator 9 from the M angular vibration signals xm(3) as a function of the angle 3 of the rotating member 15 around the rotation axis AX.

[0121] This second transformation from the angular domain to the frequency domain advantageously makes it possible to highlight the energy levels of the frequency channels fg of the vibrations of the mechanical system 10.

[0122] The frequency operator 0 can be different depending on the defect sought. The frequency operator h can for example be a Fourier transform, an envelope spectrum or another transformation.

[0123] Finally, during a first determination 160, the computer 5 determines frequency channels of tendency f as a function of the M frequency vibration signals yj, a frequency channel of tendency f( being a frequency channel f0 for which the M frequency vibration signals (V ) satisfy a selection criterion relating to a growth tendency. A frequency channel of tendency f corresponds for example to a frequency channel fg for which the energy levels of the vibrations of the mechanical system 10 are increasing and therefore likely to correspond to the appearance of a fault present on the mechanical system 10.

[0124] The first determination 160 of the trend frequency channels f may comprise several sub-steps.

[0125] During a generation sub-step 170, the computer 5 generates at least one series of amplitudes Xf associated with a frequency channel fg and comprising M amplitude values ​​belonging respectively to the M frequency vibration signals and relating to this frequency channel fg. The number of series of amplitudes Xf is equal to the number of frequency channels fg. The amplitudes of each series of amplitudes Xf characterize the vibration energy for each of the frequency channels fg.

[0126] Each amplitude value of the amplitude series Xf can be associated with the number m relative to each vibration measurement.

[0127] Alternatively, each amplitude value of the amplitude series Xf can be associated with an instant tm of a start of emission of the time-domain vibration signal xm(t) from which the amplitude value originates. This association is particularly interesting when the vibration measurements are not carried out at regular intervals, in order to take into account the irregular intervals between two vibration measurements.

[0128] Furthermore, a sub-step of filtering the amplitude values ​​of the amplitude series X / can be applied. Alternatively, such a filtering sub-step can also be applied to the series of time vibration signals xm(t), to the series of angular vibration signals xm( 3)-

[0129] Then, during a calculation sub-step 180, the calculator 5 calculates a trend indicator t ( f \ for each frequency channel f„ as a function of the 1 trend \ J (j) " series of amplitudes associated with the frequency channel fg.

[0130] Then, during a first determination sub-step 190, the calculator 5 determines frequency channels of trend f by verifying, for each frequency channel fg, whether the trend indicator i(f< ) æPon(^ ct satisfies the selection criterion.

[0131] For example, a first selection criterion may include a trend threshold. Each trend frequency channel f is then a frequency channel fg for which the trend indicator j ( f \ is greater than the trend threshold. A fre-1 trend channel \J y / trend frequency f is therefore the frequency channel fg for which the series of amplitudes is globally increasing when m varies from 1 to M, and therefore presenting an increasing trend greater than a predefined trend and corresponding to the trend threshold.

[0132] According to a second selection criterion, the frequency channels of trend f are the P frequency channels fg whose trend indicators d(f} are large, P being an integer greater than one. The frequency channels of trend f are the frequency channels fg for which the amplitude series are globally increasing when m varies from 1 to M and which comprise the M time-domain vibration signals xm(t) characterized by the strongest increases in their respective energy levels.

[0133] Only one selection criterion from among the first and second selection criteria may be used.

[0134] Alternatively, the first and second selection criteria may be used simultaneously. simultaneously and in a complementary manner. For example, when the number of frequency channels fg, whose trend indicator I{ j is greater than the trend threshold, is less than P, then only those frequency channels fg, whose trend indicator r ( f \ is greater than the trend threshold, are trend channels \Jfjj frequency channels of trend f. On the contrary, when the number of frequency channels fg, whose trend indicator j is greater than the trend threshold, is greater than P, then only the P frequency channels ,fg, whose trend indicators j ( r• \ are the largest, are frequency channels of trend f . 1 trend y J q / *

[0135] Furthermore, the calculation 180 of the trend indicator j / f \ can be decomposed into 1 (yields y J q / several sub-steps.

[0136] During a third transformation sub-step 182, the computer 5 transforms each series of amplitudes Xf into a trend series using a transformation function $ according to the relation $ _ ^Xf j' transformation function ¢ aims to linearize as much as possible the amplitude values ​​of each series of amplitudes X / . which have a growth trend consistent with the fault model. The transformation function 0 can be different depending on the fault sought. The transformation function & can for example be the unit function if the fault has a substantially “linear” growth or the function lo graph if the default has a significantly “exponential” growth.

[0137] During a construction sub-step 184, the calculator 5 constructs a trend line for each trend series by linear regression, according to the relationship

[0138] with “r”, a variable,

[0139] “(r)”, a trend line,

[0140] “Af”, a slope coefficient of the trend line,

[0141] “Bf”, an ordinate at the origin of the trend line.

[0142] During an estimation sub-step 186, the calculator 5 estimates a noise coefficient for each trend line htB ( r ), according to the relation . >2 V2 crj / \) = 77 L i gr - ( Af *.r + Bf \ \ r\J 0 / IM ^nr=l \ .> g V n -s' / I

[0143] with “yM”, the sum function with m varying from 1 to M, *-*111=1

[0144] “11”, the absolute value function.

[0145] During a calculation sub-step 188, the calculator 5 calculates the indicator of quantity / L according to the relationship T * trend trend / trenc[ as a function of the direction coefficient Ay of the trend line / îy ( r ) and the noise coefficient relative to the trend line for each channel fre-(f U--.........•

[0146] The parameters a and [3 are predetermined parameters and make it possible to weight the sensitivity to noise, a makes it possible in particular to establish an upper limit of the trend indicator / lf enij in order to limit its divergence when the noise (f] tends towards 0, r\J and whereas [3 makes it possible to adjust its sensitivity to noise.

[0147] The parameters a and [3 can be specific to each fault sought, but common to all frequency channels.

[0148] The main step of analysis 200 of the trend frequency channels f comprises the following sub-steps.

[0149] During a comparison step 210, the computer 5 compares each trend frequency channel f with linear combinations of fault frequencies fd relating to the desired fault. The fault frequencies fd are previously known and contained in a fault model associated with the desired fault. Indeed, most mechanical faults generate on a mechanical system 10 modulations of amplitudes and frequencies of the vibrations of this mechanical system 10. Consequently, for a given fault present on the mechanical system 10, specific frequencies, called “fault frequencies fd” can be excited during operation of the mechanical system 10, namely present energy levels which follow a growth “law” consistent with the desired fault.Furthermore, these fault frequencies fd vary little depending on the operating parameters of this mechanical system 10, these parameters having an influence mainly on the amplitude of the vibration signal for these fault frequencies fd.

[0150] Linear combinations of fault frequencies fd respectively comprise sums of several, or even all, of the fault frequencies fd multiplied by integer coefficients n;. The values ​​of the coefficients n; are bounded and the number of integer coefficients n; is limited. The coefficients n; are for example between -4 and 4. Consequently, the number of these linear combinations of fault frequencies fd is also limited.

[0151] These linear fault frequencies fd are such that:

[0152] "i F r ■■■tiiFf - -n k F k min < max Nk min ^k < max K -,¾ ---,¾} eZ

[0153] with k, the number of fault frequencies fd,

[0154] Fh .... Fp ..., Ffc, the k fault frequencies fd with 1 < i < k,

[0155] ni, a coefficient of the linear combination associated with the frequency Fj

[0156] N, min, a lower limit of nî,

[0157] N] max, an upper limit of ni.

[0158] Then, during a second determination step 220, the computer 5 determines fault frequency channels f , as a function of the comparison of the trend frequency channels f with linear combinations of the fault frequencies f d. A fault frequency channel f is thus determined equal to a trend frequency channel f whose differences with the linear combinations are less than a comparison threshold. The comparison threshold is low and for example equal to a frequency resolution associated with the discretized frequency vibration signals.

[0159] This second determination amounts to searching among the trend frequency channels f those making it possible to minimize the differences of each trend frequency channel f with these linear combinations of the fault frequencies fd. At the end of these comparisons, the trend frequency channels f are selected for which the result of these differences is a zero or almost zero frequency, i.e., lower than the comparison threshold, as being the fault frequency channels f .

[0160] This second main analysis step 200 therefore makes it possible to identify fault frequency channels f, for which their energy levels have an increasing trend and which correspond to frequencies characteristic of the fault sought.

[0161] During the main calculation step 300, the presence indicator Ioptimal of the fault on the mechanical system 10 is calculated.

[0162] For example, the optimal presence indicator 1 can be set to increase when the number of fault frequency channels fx increases. The fault presence alert is then triggered if the optimal presence indicator I is greater than the fault presence threshold.

[0163] For example, the Ioptimal presence indicator of the defect may be equal to a ratio of a number of fault frequency channels f by a total number of linear combinations of fault frequencies f d. The alert of the presence of a fault is then triggered if the optimal presence indicator 1 is greater than the threshold for the presence of a fault.

[0164] Thus, the presence indicator Ioptimal is standardized, namely between 0 and 1. In this way, a threshold for the presence of a single fault can be applicable to the detection of several different types of fault likely to appear on the mechanical system 10.

[0165] Furthermore, the optimal presence indicator J is zero when none of the fault frequencies fd of the fault model expresses an increasing trend and has therefore not been identified among the trend frequency channels f. Conversely, the optimal presence indicator I is equal to 1 when all the fault frequencies fd of the fault model express an increasing trend.

[0166] Furthermore, the greater the number of fault frequency channels f, the greater the presence indicator Ioptimal. Thus, the greater the presence indicator Ioptimal, the greater the risk of the presence of a fault, and the more confident one can be about the presence of the fault.

[0167] The method according to the invention therefore makes it possible to determine an optimal presence indicator which is reliable, effective and robust for detecting a fault on the mechanical system 10, from the first signs of the appearance of this fault.

[0168] Naturally, the present invention is subject to numerous variations as to its implementation. Although several embodiments have been described, it is understood that it is not conceivable to exhaustively identify all possible modes. It is of course conceivable to replace a means described by an equivalent means without departing from the scope of the present invention and the claims.

Claims

1. Claims Method for detecting a fault on a mechanical system (10), said mechanical system (10) comprising at least one rotating member (15) rotating around an axis (AX) of rotation as well as a vibration sensor (20) emitting a temporal vibration signal x( / ), an angular sensor (25) emitting a temporal angular signal varying as a function of an angular position of said rotating member (15) around said axis (AX) of rotation and a computer (5), said method comprising: - processing (100) of temporal vibration signals xm ( t ) emitted by said vibration sensor (20) comprising the following sub-steps: • emission of M temporal vibration signals xm ( t ) by said vibration sensor (20), M being greater than or equal to 2, m being a positive integer varying from 1 to M, “t” being the operating time of said mechanical system (10), • first transformation (140), with said calculator (5), of said M temporal vibration signals xm(f) into M angular vibration signals xm(9) as a function of said temporal angular signal 0m (t), “9” being an angle of said rotating member (15) around said axis (AX) of rotation, • second transformation (150), with said calculator (5), of said M angular vibration signals xm(9) into M frequency vibration signals yj by a frequency operator 9, where “fg” is a frequency channel, characterized in that said processing (100) of temporal vibration signals comprises the following sub-step: • first determination (160) of frequency channels of tendency f as a function of said M frequency vibration signals j । yj, a frequency channel of tendency ft being a frequency channel / 0 for which said M frequency vibration signals yj meet a selection criterion relating to a growth trend, and that said method comprises the following steps: - analysis (200), with said calculator (5), of said trend frequency channels f comprising the following sub-steps: • comparison (210) of each trend frequency channel f with linear combinations of fault frequencies fd relating to said fault sought, said linear combinations respectively comprising sums of several of said fault frequencies fd multiplied by integer coefficients Ni, • second determination (220) of fault frequency channels f, as a function of said comparison, a fault frequency channel f being a trend frequency channel f whose differences with said linear combinations are less than a comparison threshold, - calculation (300), with said calculator (5), of an optimal presence indicator I of said fault on the mechanical system (10) as a function of the number of said fault frequency channels f and the number of said linear combinations of said fault frequencies fd, - triggering (400) an alert of the presence of a fault based on a comparison between said indicator of the presence of said fault and a threshold of the presence of a fault.

2. Method according to claim 1, for which said first determination (160) of the trend frequency channels f comprises the following sub-steps: generation (170), with said calculator (5), of at least one series of amplitudes Xf associated with a frequency channel fg and comprising M amplitude values ​​belonging respectively to said M frequency vibration signals and relative to said frequency channel fg, the number of said series of amplitudes Xf being equal to the number of frequency channels fg, - calculation (180), with said calculator (5), of a trend indicator / ( / ) For each frequency channel fg as a function of said series of amplitudes associated with said frequency channel fg, and - first determination (190), with said calculator (5), of trend frequency channels f, each trend frequency channel f being a frequency channel fg meeting said selection criterion, said selection criterion being relative to said trend indicator t ( f 1 trend y )

3. The method of claim 2, wherein according to said selection criterion, each trend frequency channel f is a frequency channel fg for which said trend indicator t ( f \ is greater than said trend threshold. * trend ( J /

4. Method according to any one of claims 2 to 3, for which according to said selection criterion, said trend frequency channels f are the P frequency channels fg corresponding to said P largest trend indicators rff ), P being an integer greater than one.

5. Method according to any one of claims 2 to 4, for which each amplitude value of said at least one series of amplitudes Xf is associated with an instant tm of a start of emission of said temporal vibratory signal xm(t) from which said amplitude value originates.

6. Method according to any one of claims 2 to 5, for which said calculation (180) of a trend indicator j 1 trend \J g) comprises the following sub-steps: - third transformation (182) of said at least one series of amplitudes Xf by a transformation function & into at least one trend series according to the relation jq j, - construction (184) of at least one trend line from said at least one trend series by linear regression, according to the relation ]y (;• ) = Af X r + Bf -, where "r" is a variable, "ly ( r)" is a trend line, "Ay" is a slope coefficient of said trend line, "Bf" is an ordinate at the origin of said trend line, - estimation (186) of a noise coefficient ar for each trend line hf ( r ), according to the relation 2 ^2, ( A ) = 1 i IL ( «.r. - ( *rx r+Bf.) ) 1 where " " is the sum function with m varying from 1 to M, "11" is the absolute value function, - calculation (188) of said trend indicator Itrend as a function of said noise coefficient relative to said trend line for each frequency channel fe, according to the relation / ( f 1 =__________________' where "a" and "[3" are predetermined parameters.

7. A method according to claim 6, wherein said transformation function^ is the unit function or the logarithm function.

8. Method according to any one of claims 7 to 8, for which said parameters a and [3 are specific to each defect sought.

9. Method according to any one of claims 1 to 9, for which said optimal presence indicator I of said defect increases when said number of said fault frequency channels f increases and said fault presence alert is triggered if said optimal presence indicator I is greater than said fault presence threshold.

10. A method according to any one of claims 1 to 9, wherein a number of said integer coefficients Ni is limited, and values ​​of said integer coefficients Ni are bounded.

11. The method of claim 10, wherein said optimal presence indicator 1 of said fault is equal to a ratio of a number of said fault frequency channels f by a total number of linear combinations of said fault frequencies fd and said fault presence alert is triggered if said optimal presence indicator I is greater than said fault presence threshold.

12. Method according to any one of claims 1 to 11, for which said comparison threshold is equal to a frequency resolution associated with said discretized frequency vibration signals.

13. Method according to any one of claims 1 to 12, for which said at least one vibration sensor (20) emits said M temporal vibration signals xm{ / ) during a predetermined sliding duration of operation of said mechanical system (10).

14. Computer program comprising instructions which, when said program is executed, lead to implementing the method according to any one of claims 1 to 13,

15. Monitoring device (9) configured to monitor a mechanical system (10) comprising at least one rotating member (15) rotating about an axis (AX) of rotation, said monitoring device (9) comprising at least one vibration sensor (20) emitting a temporal vibration signal, an angular sensor (25) emitting a temporal angular signal varying as a function of an angular position of said rotating member (15) about said axis (AX) of rotation and a computer (5), characterized in that said monitoring device (9) is configured for implementing the method according to any one of claims 1 to 13.

16. Mechanical system (10) comprising at least one rotating member (15) rotating around an axis (AX) of rotation, characterized in that said mechanical system (10) comprises a monitoring device (9) according to claim 15.

17. Power transmission box (6) comprising a mechanical system (10) according to claim 16.

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